Using Facebook Data for Area Based Approach in El Salvador

Recent displacement trends show growing numbers of displaced population living outside of designated areas such as camp/camp like setting (traditional camps collective/transit/reception centres, informal settlements) with a majority setting in dispersed locations predominately urban and peri-urban areas such as informal settlements, unfinished buildings or interspersed in host community homes and communities, shared rooms or rental arrangements. To be able to reach, properly assess and understand local dynamics, vulnerabilities and capacities of the displaced and host populations alike, humanitarian organisations are increasingly using sub-national Area Based Approach. Area based approaches define “an area, rather than a sector or target group, as a primary entry point”. Such approach is particularly appropriate when residents in an affected area face complex, inter-related and multisectoral needs, resulting in risk of forced displacement.

Area Based Approach for Forced Displacement: Characterisation through Sensor Data

In the context of migration statistics, forced displacement is often analyzed with the prism of push and pull factors.

	Push Factor (Mitigated by intervention to address root causes within countries of origin)	Pull Factor (Mitigated by migration & Asylum policies of receiving countries)
Economic Dimension (Addressed by programme in relation with development & poverty alleviation)	Lack of public services, Unemployement, Overpopulation	More jobs, Better jobs, Higher wages, promise of a “better individual life”
Social Dimension (Addressed by programme in relation with protection)	Violence, insecurity, intolerance towards certain groups, active political or religious persecution,	Safety, tolerance, freedom
Environmental Dimension (Addressed by programme in relation with resilience & sustainability)	Climate change, natural disasters	More livable environment

Though, traditional statistical data sources are often lacking sufficient geographically-fine-grained disaggregation to inform sub national scale approach and characterization. Alternative based on sophisticated index like Inform Colombia requires extensive expert consultations and might not fully reflect the important dimension to be reflected in the context of forced displacement and migration.

Proposed methodology

New sensors provide unique abilities to capture new flow of information from social medias (Anonymized data from Facebook platform) at subnational scale through grid level information. Satellite data can pick up signals of economic activity by detecting light at night, it can pick up development status by detecting infrastructure such as roads, and it can pick up signals for individual household wealth by detecting different building footprints and roof types.

In regard to the framework above, an initial selection of globally available layers includes:

• Economics:
  • Weighted Relative Wealth Index (Facebook-RWI)
  • Social Connectedness Index (Facebook-SCI)
  • Public Services Catchment area (OSM)
• Environment:
  • Agricultural drought frequency Index (FAO-ASI)
  • Climatic Natural Risk - Flood and Cyclone (Prevention Web)
  • Geologic Natural Risk - Earthquake and Volcano (Prevention Web)
• Social:
  • Population Dependency Ratio (Facebook)
  • Movement Range data sets Facebook
  • Violence (ACLED)

Information can be compiled and aggregated at admin level 2 in order to build composite Indicators. Different areas can be then grouped together based on the values from those composite indicators. The advantage of this approach are multiple: 1. Granularity: Optimal Level of granularity 2. Availibility: Data Consistently and freely available worldwide, simplicity to obtain information, ensor based indicators are potentially less sensitive to political pressure 3. Reproducibility: Can be used in multiple countries easily and Fully automated and audited through reproducible analysis script

The resulting information can complement other traditional source of information both on quantitative (Household Survey) and qualitative (Focus Group Discussions) side.

Social:

Population Dependency Ratio (Facebook)

Population movement range

The Movement Range data sets is intended to inform on how populations are responding to physical distancing measures. In particular, there are two metrics that provide a slightly different perspective on movement trends:

• Change in Movement: looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures. The idea is to understand how much less people are moving around since the onset of the coronavirus epidemic. This is done by quantifying how much people move around by counting the number of level-16 Bing tiles (which are approximately 600 meters by 600 meters in area at the equator) they are seen in within a day. In the dataset noted all_day_bing_tiles_visited_relative_change

• Stay Put: looks at the fraction of the population that appear to stay within a small area during an entire day. This metric intends to measure this by calculating the percentage of eligible people who are only observed in a single level-16 Bing tile during the course of a day. In the dataset noted all_day_ratio_single_tile_users

Violence

Economics

Relative Wealth Index

Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty.

As explained in a dedicated paper, the Relative Wealth Index estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregated and de-identified connectivity data from Facebook.

As described in the this tutorial

• Determine which administrative unit contains the centroid of each RWI tile

• Calculate the bing tile quadkey at zoom level 14 for each point in the population density dataset and sum the population per level 14 tile

• Determine which zoom level 14 (~2.4km bing tile) corresponds to each of the smaller 30m population density tiles, and calculate the sum of population within each zoom level 14 tile.

• Calculate the total population in each administrative region using the population density dataset

• Calculate a population derived weight for each zoom level 14 RWI tile

• Use the weight value to calculate a weighted RWI value and aggregate to the administrative unit level

Social Connectedness Index

an anonymized snapshot of all active Facebook users and their friendship networks to measure the intensity of connectedness between locations. The Social Connectedness Index (SCI) is a measure of the social connectedness between different geographies. Specifically, it measures the relative probability that two individuals across two locations are friends with each other on Facebook.

http://pages.stern.nyu.edu/~jstroebe/PDF/BGHKRS_InternationalTradeSocialConnectedness_JIE.pdf

https://data.humdata.org/dataset/social-connectedness-index

Public Services

Environment:

Agricultural drought frequency Index

FAO-ASI)

Historic Agricultural Drought Frequency Maps depict the frequency of severe drought in areas where 30 percent/50 percent of the cropland has been affected. The historical frequency of severe droughts (as defined by ASI) is based on the entire ASI times series (1984-2020).

Climatic Natural Risk - Flood and Cyclone (Prevention Web)

Geologic Natural Risk - Earthquake and Volcano (Prevention Web)

Clustering analysis of different areas

Composite indicators

The polarity of a sub-indicator is the sign of the relationship between the indicator and the phenomenon to be measured (e.g., in a well-being index, “GDP per capita” has ‘positive’ polarity and “Unemployment rate” has ‘negative’ polarity). In this case, we have 2 options for such directional adjustments:

• “Negative (the higher score, the more severe)”
• “Positive (the higher score, the less severe)”

This component is accounted for during the normalization process below.

Data Normalization allows for Adjustments of distribution (similar range of variation) and scale (common scale) of sub-indicators that may reflect different units of measurement and different ranges of variation.

Due to the structure of the indicators, distinct approaches of normalization shall be considered in order to avoid having zero value that would create issues for geometric means aggregation. Different normalization methods are available through the function normalise_ci and lead to different results:

• A z-score approach method = 1 (Imposes a distribution with mean zero and variance 1). Standardized scores which are below average will become negative, implying that further geometric aggregation will be prevented.

• A min-max approach method = 2 (same range of variation [0,1]) but not same variance).This method is very sensitive to extreme values / outliers

• A ranking method method = 3. Scores are replaced by ranks – e.g. the highest score receives the first ranking position (rank 1).

Data Normalization allows for Adjustments of distribution (similar range of variation) and scale (common scale) of sub-indicators that may reflect different units of measurement and different ranges of variation.

Due to the structure of the indicators, distinct approaches of normalization shall be considered in order to avoid having zero value that would create issues for geometric means aggregation. Different normalization methods are available through the function normalise_ci and lead to different results:

• A z-score approach method = 1 (Imposes a distribution with mean zero and variance 1). Standardized scores which are below average will become negative, implying that further geometric aggregation will be prevented.

• A min-max approach method = 2 (same range of variation [0,1]) but not same variance).This method is very sensitive to extreme values/outliers

• A ranking method method = 3. Scores are replaced by ranks – e.g. the highest score receives the first ranking position (rank 1).

Correlation analysis

The investigation of the structure of simple indicators can be done by means of correlation analysis.

We will check such correlation first within each dimension, using the ggcorrplot package. An alternative approach to better visualize correlation between indicators is to represent them through a network with the ggpraph package.

’

#> From     To  Weight
#> 1     ---     2   -0.45 
#> 1     ---     3   -0.27 
#> 2     ---     3   -0.32 
#> 1     ---     4   -0.39 
#> 2     ---     4   0.21 
#> 3     ---     4   -0.16 
#> 1     ---     5   -0.23 
#> 2     ---     5   -0.09 
#> 3     ---     5   0.47 
#> 4     ---     5   -0.12 
#> 1     ---     6   -0.02 
#> 2     ---     6   -0.16 
#> 3     ---     6   0.25 
#> 4     ---     6   -0.07 
#> 5     ---     6   0.4 
#> 1     ---     7   0.06 
#> 2     ---     7   -0.16 
#> 3     ---     7   0.2 
#> 4     ---     7   -0.11 
#> 5     ---     7   0.48 
#> 6     ---     7   0.4 
#> 1     ---     8   0.01 
#> 2     ---     8   -0.19 
#> 3     ---     8   0.33 
#> 4     ---     8   -0.14 
#> 5     ---     8   0.61 
#> 6     ---     8   0.48 
#> 7     ---     8   0.8 
#> 1     ---     9   -0.12 
#> 2     ---     9   -0.14 
#> 3     ---     9   0.46 
#> 4     ---     9   -0.17 
#> 5     ---     9   0.85 
#> 6     ---     9   0.46 
#> 7     ---     9   0.73 
#> 8     ---     9   0.76

#> From     To  Weight
#> 1     ---     2   0.74 
#> 1     ---     3   1 
#> 2     ---     3   0.74 
#> 1     ---     4   0.83 
#> 2     ---     4   0.68 
#> 3     ---     4   0.82 
#> 1     ---     5   -0.45 
#> 2     ---     5   -0.39 
#> 3     ---     5   -0.46 
#> 4     ---     5   -0.28 
#> 1     ---     6   -0.12 
#> 2     ---     6   -0.22 
#> 3     ---     6   -0.12 
#> 4     ---     6   -0.12 
#> 5     ---     6   0.04 
#> 1     ---     7   -0.39 
#> 2     ---     7   -0.45 
#> 3     ---     7   -0.39 
#> 4     ---     7   -0.31 
#> 5     ---     7   0.63 
#> 6     ---     7   0.57 
#> 1     ---     8   -0.34 
#> 2     ---     8   -0.2 
#> 3     ---     8   -0.34 
#> 4     ---     8   -0.17 
#> 5     ---     8   0.85 
#> 6     ---     8   -0.39 
#> 7     ---     8   0.24

Consistency between indicators

Cronbach’s alpha, (or coefficient alpha), developed by Lee Cronbach in 1951, measures reliability (i.e. how well a test measures what it should: measure of the stability of test scores), or internal consistency.

As a rule of thumbs, a score of more than 0.7 indicates an acceptable level of consistency:

• A high level for alpha may mean that all indicators are highly correlated (meaning we have redundant indicators representing the same thing…).
• A low value for alpha may mean that there are not enough indicators or that the indicators are poorly interrelated.

’

The Cronbach Alpha measure of consistency for this combination of indicators is  0.85
.

Aggregation & Weighting

For weighting, the main issue to address is related to the concept of compensability. Namely the question is to know to what extent can we accept that the high score of an indicator go to compensate the low score of another indicator? This problem of compensability is intertwined with the issue of attribution of weights for each sub-indicator in order to calculate the final aggregation.

We can foresee that using “equal weight” (all indicators account for the same in the final index) and “arithmetic aggregation” (all indicators are substituable) is unlikely to depict the complex issue of Humanitarian Severity and is likely to comes with the risk of misrepresenting the reality.

Various methods are available within the Compind package are described below. This R package is also available through a ShinyApp. We will then share the code to use them based on our example.

Benefit of the Doubt approach (BoD)

This method is the application of Data Envelopment Analysis (DEA) to the field of composite indicators. It was originally proposed by Melyn and Moesen (1991) to evaluate macroeconomic performance. ACAPS has prepared an excellent note on The use of data envelopment analysis to calculate priority scores in needs assessments.

BoD approach offers several advantages:

• Weights are endogenously determined by the observed performances and benchmark is not based on theoretical bounds, but it’s a linear combination of the observed best performances.

• Principle is easy to communicate: since we are not sure about the right weights, we look for ”benefit of the doubt” weights (such that your overall relative performance index is as high as possible).

Directional Benefit of the Doubt (D-BoD)

Directional Benefit of the Doubt (D-BoD) model enhances non-compensatory property by introducing directional penalties in a standard BoD model in order to consider the preference structure among simple indicators. This method is described in the article Enhancing non compensatory composite indicators: a directional proposal.

Robust Benefit of the Doubt approach (RBoD)

This method is the robust version of the BoD method. It is based on the concept of the expected minimum input function of order-m so “in place of looking for the lower boundary of the support of F, as was typically the case for the full-frontier (DEA or FDH), the order-m efficiency score can be viewed as the expectation of the maximal score, when compared to m units randomly drawn from the population of units presenting a greater level of simple indicators”, Daraio and Simar (2005). This method is described with more detail in the article Robust weighted composite indicators by means of frontier methods with an application to European infrastructure endowment.

Factor analysis

This method groups together simple indicators to estimate a composite indicator that captures as much as possible of the information common to individual indicators.

Mean-Min Function (MMF)

This method is an intermediate case between arithmetic mean, according to which no unbalance is penalized, and min function, according to which the penalization is maximum. It depends on two parameters that are respectively related to the intensity of penalization of unbalance (alpha) and intensity of complementarity (beta) among indicators. “An unbalance adjustment method for development indicators”

Geometric aggregation

This method uses the geometric mean to aggregate the single indicators and therefore allows to bypass the full compensability hypothesis using geometric mean. Two weighting criteria are possible: EQUAL: equal weighting and BOD: Benefit-of-the-Doubt weights following the Puyenbroeck and Rogge (2017) approach.

Mazziotta-Pareto Index (MPI)

This method is is a non-linear composite index method which transforms a set of individual indicators in standardized variables and summarizes them using an arithmetic mean adjusted by a “penalty” coefficient related to the variability of each unit (method of the coefficient of variation penalty).

Wroclaw taxonomy method

This last method (also known as the dendric method), originally developed at the University of Wroclaw, is based on the distance from a theoretical unit characterized by the best performance for all indicators considered; the composite indicator is therefore based on the sum of euclidean distances from the ideal unit and normalized by a measure of variability of these distance (mean + 2*std).

Visualise output

In a table

Composite with different algorithm

	Benef_Doubt	Benef_Doubt_Rob	Benef_Doubt_Dir	Factor	Mean_Geom	Mean_Min	Mazziotta_Pareto	Wroclaw
SV0101	0.9194611	1.5242046	0.8949529	-0.0947139	NaN	90661.70	844464.7	0.4947580
SV0102	0.7213726	1.2400413	0.6808292	0.0437439	NaN	53286.75	927216.5	0.4947580
SV0103	0.8317471	1.5207861	0.7969113	0.2336051	NaN	6901017.19	124194446.7	0.4947580
SV0104	0.7894281	1.3239778	0.7313262	0.1263475	NaN	17129831.00	308324843.5	0.4947580
SV0105	1.0000000	2.1030404	1.0000000	1.3085004	NaN	6173700.21	111096123.5	0.4947580
SV0106	0.6864198	1.0260029	0.6680960	-0.1581458	NaN	5768318.54	103785007.2	0.4947580
SV0107	0.6864198	1.0661544	0.6351077	-0.3743122	NaN	97691.59	930895.2	0.4947580
SV0108	0.7281155	1.2490068	0.7038459	-0.3360233	NaN	156000.77	2792156.3	0.4947580
SV0109	0.6864198	1.2462063	0.6435774	-0.1127151	NaN	90660.21	844479.4	0.4947580
SV0110	0.6864198	0.9316551	0.7033525	-0.0827130	NaN	53286.04	927229.3	0.4947580
SV0111	0.6894908	1.2413580	0.6501203	-0.4249942	NaN	6901016.30	124194462.9	0.4947580
SV0112	0.6864198	NaN	0.6605382	-0.3957867	0.00000	17129823.07	308324911.6	0.4947580
SV0201	0.9818478	1.5723288	0.7834489	0.2962558	NaN	12052786.93	216884544.6	0.1016491
SV0209	0.9818478	1.4432037	0.7954109	0.1339835	NaN	11260509.60	202627140.1	0.1016491
SV0202	1.0000000	2.0398984	1.0000000	1.2046493	NaN	286092.74	3246994.2	0.1016491
SV0203	1.0000000	1.7788452	1.0000000	0.2271373	NaN	541778.01	9730169.6	0.1016491
SV0204	0.9818478	-0.6996497	0.7097491	0.0850992	NaN	262393.74	2886842.4	0.1016491
SV0205	0.9818478	1.3661823	0.7878894	0.3141720	NaN	183625.62	3254450.9	0.1016491
SV0206	0.9980557	2.0182485	0.9911112	-0.0598407	NaN	13472858.05	242452769.2	0.1016491
SV0207	0.9818478	1.3145737	0.7991882	0.2837598	NaN	33441626.06	601931262.1	0.1016491
SV0208	0.9818478	1.4440589	0.7413614	0.0608902	NaN	12052787.01	216884543.1	0.1016491
SV0301	1.0000000	1.7432539	1.0000000	0.2304805	NaN	11393148.91	205012755.3	0.0912800
SV0302	1.0000000	-10.2277352	1.0000000	0.2957699	NaN	508448.61	6970977.3	0.0912800
SV0303	1.0000000	1.3842487	1.0000000	0.5359961	NaN	1091771.20	19629116.0	0.0912800
SV0307	1.0000000	2.2839001	1.0000000	0.7622844	NaN	461390.75	6177244.1	0.0912800
SV0304	1.0000000	1.4972162	1.0000000	0.3695036	NaN	368837.87	6586149.4	0.0912800
SV0305	1.0000000	2.5213984	1.0000000	0.9731282	NaN	13632631.32	245287463.4	0.0912800
SV0306	1.0000000	1.6139854	1.0000000	0.8792913	NaN	33835468.62	609019732.7	0.0912800
SV0308	1.0000000	1.4142465	1.0000000	0.5576782	NaN	12195827.57	219418299.7	0.0912800
SV0309	1.0000000	1.6537204	1.0000000	0.7264762	NaN	11393159.67	205012729.3	0.0912800
SV0310	1.0000000	2.5220636	1.0000000	1.0217532	NaN	508450.37	6970952.4	0.0912800
SV0399	1.0000000	2.3045169	1.0000000	0.5178707	NaN	1091766.50	19629120.4	0.0912800
SV0311	1.0000000	1.2292026	1.0000000	0.6033997	NaN	461390.00	6177254.4	0.0912800
SV0312	1.0000000	1.6576023	1.0000000	0.4986173	NaN	368839.39	6586121.5	0.0912800
SV0313	1.0000000	2.0021155	1.0000000	0.2535241	NaN	13632631.50	245287459.4	0.0912800
SV0314	1.0000000	1.4281549	1.0000000	0.3997261	NaN	33835466.62	609019769.8	0.0912800
SV0315	1.0000000	1.6335552	1.0000000	0.4907681	NaN	12195827.39	219418303.1	0.0912800
SV0316	1.0000000	2.0211925	1.0000000	0.1758513	NaN	11393155.38	205012739.9	0.0912800
SV0317	1.0000000	-9.0815358	1.0000000	0.3093379	NaN	508449.19	6970968.9	0.0912800
SV0318	1.0000000	1.8851446	1.0000000	0.4706329	NaN	1091757.64	19629145.6	0.0912800
SV0319	1.0000000	2.0288730	1.0000000	0.8214725	NaN	461391.04	6177240.1	0.0912800
SV0320	1.0000000	1.4017035	1.0000000	0.7179746	NaN	368839.33	6586122.5	0.0912800
SV0321	1.0000000	7.0530124	1.0000000	0.9429774	67.64960	13632630.89	245287471.8	0.0912800
SV0322	1.0000000	1.5857098	1.0000000	0.3936039	NaN	33835466.91	609019764.4	0.0912800
SV0323	1.0000000	NaN	1.0000000	0.6438580	0.00000	12195827.76	219418296.0	0.0912800
SV0324	1.0000000	1.7251497	1.0000000	0.2190583	NaN	11393151.08	205012750.7	0.0912800
SV0325	1.0000000	2.1861594	1.0000000	1.2509124	NaN	508450.12	6970956.0	0.0912800
SV0326	1.0000000	-17.0510933	1.0000000	0.3333679	NaN	1091774.81	19629109.4	0.0912800
SV0327	1.0000000	1.6840523	1.0000000	0.8968648	NaN	461390.64	6177245.5	0.0912800
SV0328	1.0000000	1.6252072	1.0000000	0.8258141	NaN	368839.45	6586120.3	0.0912800
SV0329	1.0000000	NaN	1.0000000	0.8509805	0.00000	13632630.64	245287477.1	0.0912800
SV0330	1.0000000	1.3609000	1.0000000	0.6479529	NaN	33835467.29	609019757.3	0.0912800
SV0331	1.0000000	1.9745691	1.0000000	0.8406961	NaN	12195828.18	219418288.6	0.0912800
SV0332	1.0000000	2.2907136	1.0000000	0.8405965	NaN	11393169.17	205012709.6	0.0912800
SV0333	1.0000000	1.6824563	1.0000000	0.4109148	NaN	508448.87	6970973.7	0.0912800
SV0401	0.7363430	2.4040368	0.8063895	-0.0896493	60.36462	190191.06	3409409.0	0.4193858
SV0402	0.9384300	3.1104419	0.9291990	0.2470026	67.82130	109151.00	1027068.4	0.4193858
SV0403	0.7207402	2.3384000	0.7928957	-0.2480113	44.91077	64594.07	1138355.8	0.4193858
SV0404	0.6882506	2.5185922	0.7804299	-0.1631927	44.38233	8160548.53	146866472.9	0.4193858
SV0405	0.5865221	1.0474520	0.6993295	-0.4024191	NaN	20256457.80	364606924.5	0.4193858
SV0406	0.5865221	0.9511994	0.7115131	-0.4104881	NaN	7300431.40	131378020.1	0.4193858
SV0407	1.0000000	NaN	1.0000000	0.2982969	0.00000	6820796.06	122737185.4	0.4193858
SV0408	0.5865221	0.9301553	0.6881748	-0.4872122	NaN	117705.49	1134006.1	0.4193858
SV0409	0.6255702	1.1048449	0.6727310	-0.5944882	NaN	190158.65	3409502.4	0.4193858
SV0410	0.6523534	1.2894034	0.6910279	-0.4693073	NaN	109149.09	1027087.0	0.4193858
SV0411	0.7197160	2.3652624	0.7951742	-0.1968118	55.58076	64593.90	1138358.9	0.4193858
SV0412	0.7734272	1.3499600	0.8217365	0.1755861	NaN	8160548.99	146866464.0	0.4193858
SV0413	0.6376931	1.1710264	0.7523449	-0.2369856	NaN	20256458.05	364606919.8	0.4193858
SV0414	0.8193156	2.7977535	0.8474973	0.0531996	66.81184	7300432.39	131378002.7	0.4193858
SV0415	0.6856693	1.3935058	0.7039253	-0.9587641	NaN	6820779.66	122737288.6	0.4193858
SV0416	0.5865221	1.0998363	0.6276394	-0.8198250	NaN	117704.74	1134013.6	0.4193858
SV0501	0.9529370	2.3387490	0.9031420	0.5114316	62.53936	77433.70	1380282.1	0.7887146
SV0502	0.6172686	1.1184773	0.6661029	-0.4765015	NaN	37910.79	408355.6	0.7887146
SV0503	0.7629616	1.5303225	0.7145333	-0.1451315	NaN	26574.24	456479.3	0.7887146
SV0504	0.8140028	1.9606642	0.7792984	0.0070913	NaN	1989391.30	35794353.1	0.7887146
SV0505	0.5453983	1.4096827	0.6142868	-1.0215120	NaN	4937701.38	88869715.4	0.7887146
SV0506	0.6211120	0.9885191	0.6573441	-0.4700114	NaN	1779864.93	32016677.4	0.7887146
SV0507	0.6284139	1.0806481	0.6755037	-0.3179317	NaN	1663306.95	29904117.5	0.7887146
SV0508	0.5969643	1.1227694	0.6442096	-0.6736342	NaN	41385.65	458081.1	0.7887146
SV0509	0.7595268	1.2112871	0.7340787	-0.4183464	NaN	77400.04	1380403.4	0.7887146
SV0510	1.0000000	1.6624608	1.0000000	0.0435492	NaN	37912.42	408337.5	0.7887146
SV0511	0.7198485	NaN	0.7508032	-0.1133161	0.00000	26573.36	456494.9	0.7887146
SV0512	0.8162090	1.3058060	0.7842189	-0.3513348	NaN	1989391.33	35794352.2	0.7887146
SV0513	0.8160783	1.2785780	0.7826743	-0.3827027	NaN	4937703.39	88869678.0	0.7887146
SV0514	0.7678343	1.7335222	0.7518938	0.1739094	NaN	1779866.12	32016655.5	0.7887146
SV0515	0.5481020	0.8303749	0.6414871	-0.4388751	NaN	1663305.90	29904123.0	0.7887146
SV0516	0.5695234	0.9528646	0.6312332	-0.5973940	NaN	41385.89	458078.3	0.7887146
SV0517	0.6755459	1.0882556	0.6719388	-0.5780187	NaN	77402.93	1380399.7	0.7887146
SV0518	0.5358025	0.9935136	0.6049538	-0.7788144	NaN	37909.89	408365.7	0.7887146
SV0519	0.6203990	1.1645161	0.6625409	-0.5969692	NaN	26571.48	456528.3	0.7887146
SV0520	0.5899952	1.2698285	0.6453706	-0.7191090	NaN	1989390.05	35794375.9	0.7887146
SV0521	0.5492905	0.9692710	0.6344361	-0.5790601	NaN	4937701.30	88869716.7	0.7887146
SV0522	0.7597752	1.6983239	0.7474815	0.1682765	51.94887	1779866.04	32016657.2	0.7887146
SV0601	0.7916144	2.2618488	0.8190611	0.2480160	61.33395	4296612.59	77301653.2	0.6001616
SV0602	0.6902223	1.6361296	0.7119886	-0.1338610	NaN	60748.93	542858.2	0.6001616
SV0603	0.5654321	NaN	0.6203429	-0.7049447	NaN	86315.41	1539736.1	0.6001616
SV0604	0.5654321	NaN	0.6350955	-0.6328661	0.00000	56742.98	502391.2	0.6001616
SV0605	0.7268183	1.3550014	0.7174943	-0.1110973	NaN	29609.43	509370.8	0.6001616
SV0606	0.6337676	0.9188767	0.6998413	-0.1001332	NaN	5140125.93	92506531.2	0.6001616
SV0607	0.5654321	0.7364278	0.5954317	-0.8036686	NaN	12759052.29	229653676.8	0.6001616
SV0608	0.5654321	0.6375105	0.6338615	-0.4960791	NaN	4598432.32	82749515.3	0.6001616
SV0609	0.5654321	0.7019601	0.6215832	-0.6087410	NaN	4296600.05	77301722.5	0.6001616
SV0610	0.6208852	1.0249234	0.6521080	-0.4897613	NaN	60748.45	542862.7	0.6001616
SV0611	0.7409518	2.1654213	0.7757908	-0.0236248	51.99007	86323.39	1539668.1	0.6001616
SV0612	0.6135406	1.1103117	0.6544456	-0.4908149	NaN	56742.54	502395.2	0.6001616
SV0613	0.5715944	0.9323178	0.6424511	-0.4720511	NaN	29607.13	509411.6	0.6001616
SV0614	0.7394410	1.3358801	0.7305990	-0.3360184	NaN	5140127.04	92506510.2	0.6001616
SV0615	0.5882616	0.9174340	0.6775691	-0.1936678	NaN	12759054.16	229653644.1	0.6001616
SV0616	0.6339448	1.1807947	0.6610592	-0.3309438	NaN	4598433.15	82749500.7	0.6001616
SV0617	0.6066436	0.9380809	0.6718918	-0.2773434	NaN	4296607.04	77301703.6	0.6001616
SV0618	0.7718140	1.6203013	0.7727030	0.3178601	NaN	60750.14	542846.7	0.6001616
SV0619	0.6109715	1.1849127	0.6729983	-0.1910650	NaN	86308.31	1539744.0	0.6001616
SV0620	0.6957287	1.2264288	0.6956683	-0.5067066	NaN	56743.35	502387.7	0.6001616
SV0621	0.5821554	0.9515492	0.6415286	-0.4943263	NaN	29605.87	509434.1	0.6001616
SV0622	0.8428911	1.4158023	0.8166558	-0.6480729	NaN	5140127.41	92506503.2	0.6001616
SV0701	0.9135802	1.2502103	0.7641091	0.2764584	NaN	22227123.25	400071500.0	0.3715732
SV0702	0.9135802	1.1820301	0.7229748	0.1982383	NaN	8011358.84	144143873.2	0.3715732
SV0703	0.9135802	1.1827920	0.6960414	0.0943616	NaN	7484783.76	134667468.8	0.3715732
SV0704	0.9135802	1.4895872	0.8394947	0.2409350	NaN	243225.55	3022968.1	0.3715732
SV0705	0.9135802	1.3651643	0.8042873	0.2650405	NaN	491543.14	8827230.1	0.3715732
SV0706	0.9135802	1.1548116	0.7166341	0.1210013	NaN	221865.27	2679626.6	0.3715732
SV0707	0.9135802	1.1437153	0.7998770	0.4344638	NaN	166600.18	2952553.5	0.3715732
SV0708	0.9135802	1.7987922	0.8220317	0.4696630	NaN	8955144.11	161139023.0	0.3715732
SV0709	0.9135802	1.0381316	0.7011597	0.1220597	NaN	22227122.15	400071520.5	0.3715732
SV0710	0.9857579	2.0442338	0.9780034	1.0337916	NaN	8011360.59	144143841.9	0.3715732
SV0711	0.9135802	1.1325333	0.7427393	0.1647848	NaN	7484786.78	134667462.3	0.3715732
SV0712	0.9135802	1.3338723	0.7736856	0.1423549	NaN	243225.40	3022969.9	0.3715732
SV0713	0.9135802	1.3778836	0.7329385	0.0925239	NaN	491552.77	8827215.0	0.3715732
SV0715	0.9135802	1.3018435	0.8004699	0.4089868	NaN	221865.39	2679625.2	0.3715732
SV0716	0.9135802	1.6236206	0.8324595	0.4867143	NaN	166601.75	2952524.8	0.3715732
SV0717	0.9135802	1.1536286	0.7642985	0.3818127	NaN	8955143.28	161139038.3	0.3715732
SV0718	0.9135802	0.9934119	0.7295670	0.2330178	NaN	22227121.87	400071525.8	0.3715732
SV0801	1.0000000	1.1170882	1.0000000	-0.1963914	NaN	13557627.72	243999178.4	0.0206319
SV0802	1.0000000	1.2522006	1.0000000	-0.3737105	NaN	12666778.92	227952872.8	0.0206319
SV0803	1.0000000	1.2045589	1.0000000	-0.2544677	NaN	171376.19	1528157.0	0.0206319
SV0804	1.0000000	1.4152617	1.0000000	-0.0598076	NaN	236304.49	4235752.1	0.0206319
SV0805	1.0000000	1.2693748	1.0000000	-0.2056170	NaN	160558.65	1419891.3	0.0206319
SV0806	1.0000000	1.3594204	1.0000000	-0.0768428	NaN	80485.91	1410117.5	0.0206319
SV0807	1.0000000	-0.7396564	1.0000000	-0.3355782	NaN	15155150.60	272761134.1	0.0206319
SV0808	1.0000000	1.1638558	1.0000000	-0.1523468	NaN	37619489.07	677136999.3	0.0206319
SV0809	1.0000000	1.2595183	1.0000000	-0.3395796	NaN	13557627.62	243999180.6	0.0206319
SV0810	1.0000000	1.4690624	1.0000000	-0.2257705	NaN	12666775.16	227952876.3	0.0206319
SV0811	1.0000000	1.2865198	1.0000000	-0.2428388	NaN	171375.58	1528162.9	0.0206319
SV0812	1.0000000	1.6906028	1.0000000	-0.0219774	NaN	236305.57	4235748.3	0.0206319
SV0813	1.0000000	1.3381579	1.0000000	-0.1199302	NaN	160558.73	1419890.5	0.0206319
SV0814	1.0000000	1.1832419	1.0000000	-0.2496585	NaN	80484.55	1410142.1	0.0206319
SV0815	1.0000000	2.1408562	1.0000000	0.2235325	NaN	15155152.49	272761098.6	0.0206319
SV0816	1.0000000	-3.4016370	1.0000000	-0.4070202	NaN	37619488.39	677137011.9	0.0206319
SV0817	1.0000000	1.2096804	1.0000000	-0.1433825	NaN	13557627.81	243999176.8	0.0206319
SV0818	1.0000000	NaN	1.0000000	-0.3249928	0.00000	12666786.38	227952863.3	0.0206319
SV0819	1.0000000	1.6195324	1.0000000	0.0234642	NaN	171376.83	1528151.0	0.0206319
SV0820	1.0000000	2.5516592	1.0000000	0.0785246	49.02393	236325.48	4235704.4	0.0206319
SV0821	1.0000000	1.3278579	1.0000000	-0.1138065	NaN	160559.41	1419884.3	0.0206319
SV0822	1.0000000	1.1367817	1.0000000	-0.1886251	NaN	80482.81	1410163.2	0.0206319
SV0823	1.0000000	1.7700412	1.0000000	-0.2331888	NaN	15155152.21	272761103.9	0.0206319
SV0824	1.0000000	12.0977330	1.0000000	-0.4363100	NaN	37619486.40	677137030.2	0.0206319
SV0825	1.0000000	1.3535501	1.0000000	-0.4753351	NaN	13557627.45	243999183.6	0.0206319
SV0826	1.0000000	1.1767180	1.0000000	0.1652175	NaN	12666784.03	227952857.8	0.0206319
SV0901	0.8197531	0.9921982	0.7318074	0.2529125	NaN	140089.29	1743284.7	0.6031695
SV0902	0.8197531	1.0076075	0.6955033	0.0842369	NaN	283665.81	5087175.4	0.6031695
SV0903	0.8197531	1.1041848	0.7146888	0.1419815	NaN	127740.02	1545402.2	0.6031695
SV0904	0.8197531	0.9879240	0.7049323	0.1158635	NaN	96469.76	1696064.4	0.6031695
SV0905	0.8200063	1.3497935	0.7700529	-0.0095502	NaN	5087534.98	91539213.6	0.6031695
SV0906	0.8197531	1.0546946	0.7074183	0.1685485	NaN	12627252.39	227275562.5	0.6031695
SV0907	0.8772041	1.4613625	0.8238734	0.4529798	NaN	4551449.69	81883001.8	0.6031695
SV0908	0.8197531	1.0086038	0.7047868	0.1418203	NaN	4252607.14	76494026.0	0.6031695
SV0909	0.8197531	1.1700389	0.7411889	0.2964299	NaN	140089.89	1743276.8	0.6031695
SV0910	1.0000000	2.9555485	1.0000000	1.0906217	86.22503	283696.63	5087061.8	0.6031695
SV0911	0.8197531	1.0712359	0.6565612	-0.0809925	NaN	127738.95	1545415.6	0.6031695
SV0912	0.9257197	1.6916297	0.8991947	0.8606907	NaN	96472.35	1696017.3	0.6031695
SV0913	0.8197531	-0.8643748	0.6896325	0.0058682	NaN	5087532.92	91539252.2	0.6031695
SV0914	0.8197531	1.2796457	0.6554429	-0.1778691	NaN	12627252.26	227275564.9	0.6031695
SV0915	0.9078385	1.3481680	0.8781513	0.5934302	NaN	4551449.70	81883001.8	0.6031695
SV0916	0.8197531	1.1864942	0.7064260	0.0588150	NaN	4252604.43	76494031.3	0.6031695
SV0917	1.0000000	1.7651749	1.0000000	0.0742432	NaN	140088.84	1743290.6	0.6031695
SV0918	0.9813852	2.0134023	0.9757505	0.9941495	NaN	283671.12	5087150.9	0.6031695
SV0919	0.8197531	1.4795201	0.6622318	-0.3344454	NaN	127739.21	1545412.2	0.6031695
SV0920	0.8197531	1.1326478	0.7221003	0.2290888	NaN	96468.98	1696078.5	0.6031695
SV1001	0.7037774	1.3175936	0.7592891	-0.3572888	NaN	721160.90	12970963.1	0.8646296
SV1002	0.9125006	1.7205768	0.8478968	-0.0317179	44.97484	1789679.23	32208184.2	0.8646296
SV1003	1.0000000	2.3308096	1.0000000	0.8852041	57.05951	645320.82	11599952.0	0.8646296
SV1004	1.0000000	2.3387191	1.0000000	0.6981284	54.99719	603236.39	10831516.0	0.8646296
SV1019	0.7939786	1.7177703	0.7941934	0.1438960	45.14239	26306.72	350529.4	0.8646296
SV1005	0.5584869	1.0661489	0.6579609	-0.8638212	NaN	55849.55	995519.3	0.8646296
SV1006	0.6102633	1.1096382	0.6965141	-0.6509299	NaN	23791.31	311853.1	0.8646296
SV1007	0.7694303	1.6237757	0.7640597	0.0391638	44.72528	19103.39	330671.6	0.8646296
SV1099	0.4064082	0.7838809	0.6225419	-0.8132075	19.46150	721158.97	12970999.5	0.8646296
SV1008	0.9437146	1.9419446	0.9334826	0.2704000	48.59699	1789678.14	32208204.4	0.8646296
SV1009	0.6966340	1.2455084	0.7160466	-0.5515810	NaN	645318.60	11599992.3	0.8646296
SV1010	0.7146546	1.3574158	0.7307261	-0.9128488	NaN	603221.52	10831635.4	0.8646296
SV1011	0.6227347	1.1074493	0.7257810	-0.5441312	NaN	26303.49	350574.4	0.8646296
SV1012	1.0000000	2.2459372	1.0000000	0.6742230	NaN	55876.87	995441.1	0.8646296
SV1013	0.7940421	1.6656469	0.7685819	-0.2185450	NaN	23792.52	311836.9	0.8646296
SV1014	0.9051841	1.8479591	0.8370063	0.1075853	48.02604	19105.06	330641.8	0.8646296
SV1015	0.5967680	1.2989784	0.7004744	-0.3235243	NaN	721160.22	12970976.2	0.8646296
SV1016	0.6369158	1.2279354	0.7094783	-0.2529024	38.39021	1789677.59	32208214.6	0.8646296
SV1017	0.8044141	1.5875002	0.7351460	-0.0426384	44.30140	645319.59	11599974.2	0.8646296
SV1018	0.7345991	1.4221880	0.7195339	-0.4659205	NaN	603232.14	10831594.9	0.8646296
SV1101	0.6975749	1.2999396	0.6865261	-0.4423312	NaN	123088.77	1183243.4	0.3957102
SV1102	0.7832152	1.2982627	0.7725125	0.1541990	NaN	198489.75	3556781.7	0.3957102
SV1103	0.7881887	4.3937118	0.8346381	0.4074556	51.42843	114178.86	1071696.9	0.3957102
SV1104	0.6864198	1.3418635	0.6562222	-0.5680995	NaN	67600.41	1184351.3	0.3957102
SV1105	0.6864198	1.4690752	0.6461463	-0.6334632	NaN	8556414.80	153988455.8	0.3957102
SV1106	0.6864198	1.2178663	0.7312127	-0.0421299	NaN	21238990.80	382289739.8	0.3957102
SV1107	0.7538812	1.3856515	0.7343583	-0.1494876	NaN	7654576.51	137748867.9	0.3957102
SV1108	0.7761589	1.3925177	0.7687788	-0.4149154	NaN	7151763.80	128687156.1	0.3957102
SV1109	0.6864198	1.4743362	0.6401585	-0.7145474	NaN	123088.45	1183246.7	0.3957102
SV1110	0.7869416	2.6343177	0.8164785	0.2030698	64.31205	198519.75	3556692.2	0.3957102
SV1111	0.6864198	1.0854118	0.6482128	-0.4349362	NaN	114177.37	1071711.3	0.3957102
SV1112	0.7426199	NaN	0.7890106	-0.0154053	0.00000	67601.42	1184332.9	0.3957102
SV1113	0.6864198	1.0953235	0.7156986	-0.0757677	NaN	8556414.45	153988462.9	0.3957102
SV1201	0.6320988	0.8472507	0.6326526	-0.2960383	NaN	8065109.61	145160735.1	0.7133090
SV1202	0.8505454	1.2511691	0.8203728	-0.2261471	NaN	2906935.66	52300720.7	0.7133090
SV1203	0.6901312	1.1020180	0.6763247	-0.2238446	NaN	2716365.98	48853274.2	0.7133090
SV1204	0.7050162	1.1671677	0.6892715	-0.1690239	NaN	52170.66	518888.4	0.7133090
SV1205	0.7181056	1.1984668	0.6919186	0.0310242	NaN	88432.36	1576877.7	0.7133090
SV1299	0.6320988	0.7625390	0.6699700	-0.2296461	NaN	48187.41	466906.8	0.7133090
SV1298	0.6320988	8.8206145	0.5986130	-0.6249415	NaN	30446.57	519715.3	0.7133090
SV1206	0.6320988	1.2436113	0.6315013	-0.5259910	NaN	3249282.07	58469149.9	0.7133090
SV1207	0.8734902	1.6234447	0.8357310	-0.6999676	NaN	8065111.27	145160704.3	0.7133090
SV1208	0.6320988	0.7048255	0.6349923	-0.3411517	NaN	2906934.96	52300732.4	0.7133090
SV1209	0.7764533	1.8948880	0.7834278	0.2288746	55.13936	2716372.91	48853232.7	0.7133090
SV1210	0.9563663	1.4910862	0.9424479	-0.1168941	NaN	52170.84	518886.6	0.7133090
SV1211	0.6320988	0.7708205	0.6518670	-0.2325438	NaN	88439.55	1576868.1	0.7133090
SV1212	0.6786103	1.0269437	0.6676379	-0.1542049	NaN	48187.62	466904.6	0.7133090
SV1213	0.7353068	1.2654663	0.7123007	-0.4827705	NaN	30461.23	519637.1	0.7133090
SV1301	0.7110361	1.3701563	0.7019773	-0.3583585	NaN	4265080.42	76752735.3	0.6524864
SV1302	0.6361289	1.2052268	0.6566669	-0.3793201	NaN	10586624.58	190550153.7	0.6524864
SV1303	0.5889095	1.2903771	0.6457008	-0.6717706	NaN	3815642.83	68656615.7	0.6524864
SV1304	0.5456790	1.0160315	0.6053079	-0.7958604	NaN	3565159.33	64137141.0	0.6524864
SV1305	0.7357880	1.3056987	0.7221603	-0.4498431	NaN	73936.58	765691.7	0.6524864
SV1306	0.5937213	0.9788989	0.6530225	-0.4318472	NaN	129926.27	2325519.2	0.6524864
SV1307	0.7163359	1.3397625	0.7050064	-0.2226080	NaN	68123.37	686087.8	0.6524864
SV1308	0.7241744	1.3218159	0.7187034	-0.5524499	NaN	44276.06	774163.4	0.6524864
SV1309	0.9232063	2.5840442	0.9195016	0.5673967	73.79356	4265080.67	76752732.6	0.6524864
SV1310	0.7095852	1.5440643	0.7162713	-0.0007244	NaN	10586625.15	190550143.2	0.6524864
SV1311	0.7177671	1.3115432	0.7069371	-0.6396584	NaN	3815643.29	68656608.2	0.6524864
SV1312	0.5926358	1.1282502	0.6642266	-0.2625348	NaN	3565176.13	64137098.7	0.6524864
SV1313	0.6236773	1.1938280	0.6590482	-0.5704541	NaN	73936.27	765695.1	0.6524864
SV1314	0.5647067	0.8009826	0.6720331	-0.2939350	NaN	129938.75	2325492.5	0.6524864
SV1315	0.7443018	1.2325811	0.7201243	-0.7332861	NaN	68123.27	686088.6	0.6524864
SV1316	0.7705039	2.0847045	0.7950567	0.1482368	60.91138	44277.79	774132.0	0.6524864
SV1401	0.8580046	1.6137039	0.8081672	0.3384277	NaN	6058322.76	109013909.8	0.5450798
SV1402	0.8246914	1.4184870	0.6764192	-0.2023565	NaN	15037184.65	270654168.0	0.5450798
SV1403	0.8246914	-6.6297819	0.6728499	-0.0282550	NaN	5419884.12	97515429.1	0.5450798
SV1405	0.8444414	1.3111072	0.7873514	0.2868357	NaN	5063979.44	91098391.6	0.5450798
SV1406	0.8972752	3.1951770	0.8749481	0.7588240	65.87715	141181.62	1654665.0	0.5450798
SV1407	0.8246914	1.2247105	0.7615157	0.3808378	NaN	274316.15	4918562.1	0.5450798
SV1408	0.8248011	1.3588880	0.7612870	-0.0016599	NaN	129184.03	1469085.4	0.5450798
SV1409	0.8251224	1.1544322	0.7574377	0.2973474	NaN	93334.22	1639040.5	0.5450798
SV1410	0.8246914	1.2887059	0.6784833	-0.1698608	NaN	6058321.16	109013939.1	0.5450798
SV1411	0.8246914	1.5200657	0.7718324	0.3762832	NaN	15037185.49	270654152.5	0.5450798
SV1412	0.8246914	0.9667015	0.7265914	0.1274328	NaN	5419884.82	97515417.1	0.5450798
SV1413	0.8659087	1.5467050	0.8239596	0.6125127	NaN	5063988.04	91098359.4	0.5450798
SV1414	0.8748150	1.5844662	0.8461429	0.7214697	NaN	141180.86	1654674.5	0.5450798
SV1415	0.8246914	1.2768033	0.6976851	-0.0414692	NaN	274296.74	4918614.7	0.5450798
SV1416	0.8246914	0.8937032	0.7154690	0.1941221	NaN	129183.64	1469090.8	0.5450798
SV1417	0.8828154	1.4291488	0.8430895	0.7507730	NaN	93335.96	1639008.9	0.5450798
SV1418	0.8246914	0.9293655	0.6988305	0.1074128	NaN	6058321.37	109013935.7	0.5450798
SV1419	0.8684024	1.4608429	0.8228834	0.4506804	NaN	15037186.27	270654138.0	0.5450798
SV1420	1.0000000	NaN	1.0000000	0.7515436	0.00000	5419885.98	97515395.7	0.5450798
SV1421	0.8246914	0.9849501	0.7388802	0.2740771	NaN	5063978.50	91098397.2	0.5450798
SV1422	0.8246914	1.0829523	0.6957235	0.1035263	NaN	141179.09	1654696.1	0.5450798
SV1423	0.8593880	1.7157558	0.8248914	0.4276728	NaN	274303.71	4918585.2	0.5450798
SV0714	0.9135802	1.4345661	0.7926578	0.1688549	NaN	221865.59	2679622.6	0.3715732
SV1404	0.8246914	1.2659651	0.7482462	0.0728259	NaN	93335.04	1639025.6	0.5450798

As we can see some of the potential aggregation algorithm are not providing results from some location. We will therefore exclude them from the rest of the analysis.

Differences between algorithms

The various Index can be normalised again on a 0 to 1 scale in order to be compared. A specific treatment if necessary for index based on Factor analysis

Location Ranking with different algorithms

	Benef_Doubt	Benef_Doubt_Dir	Factor	Mean_Min	Mazziotta_Pareto	Wroclaw
SV0101	180.0	186	158	56	36	148.0
SV0102	84.0	60	183	27	38	146.0
SV0103	147.0	158	212	197	196	149.0
SV0104	110.0	113	196	252	251	144.0
SV0105	231.0	231	266	193	193	150.0
SV0106	64.5	47	149	189	189	145.0
SV0107	56.5	17	110	63	40	142.5
SV0108	87.0	80	117	85	94	142.5
SV0109	60.5	22	156	55	37	141.0
SV0110	60.5	79	162	26	39	147.0
SV0111	67.0	28	102	196	197	139.0
SV0112	60.5	40	105	251	252	140.0
SV0201	190.0	147	219	222	223	200.0
SV0209	190.0	157	198	216	216	202.0
SV0202	231.0	231	264	114	100	206.0
SV0203	231.0	231	210	130	130	205.0
SV0204	188.0	89	191	107	95	198.0
SV0205	190.0	150	223	94	101	203.0
SV0206	195.0	195	166	236	236	204.0
SV0207	192.5	159	217	259	259	201.0
SV0208	192.5	121	189	223	222	199.0
SV0301	231.0	231	211	217	221	208.0
SV0302	231.0	231	218	125	127	207.0
SV0303	231.0	231	240	142	141	219.0
SV0307	231.0	231	249	121	116	229.0
SV0304	231.0	231	228	115	122	212.0
SV0305	231.0	231	261	243	242	233.0
SV0306	231.0	231	256	263	260	228.0
SV0308	231.0	231	241	225	226	224.0
SV0309	231.0	231	247	220	218	226.0
SV0310	231.0	231	263	129	123	239.0
SV0399	231.0	231	239	141	142	231.0
SV0311	231.0	231	243	119	118	222.0
SV0312	231.0	231	237	117	120	221.0
SV0313	231.0	231	214	244	241	216.0
SV0314	231.0	231	231	260	263	214.0
SV0315	231.0	231	236	224	227	223.0
SV0316	231.0	231	205	219	219	220.0
SV0317	231.0	231	222	127	125	210.0
SV0318	231.0	231	234	140	143	218.0
SV0319	231.0	231	251	122	115	235.0
SV0320	231.0	231	246	116	121	227.0
SV0321	231.0	231	260	242	243	237.0
SV0322	231.0	231	230	261	262	211.0
SV0323	231.0	231	244	226	225	234.0
SV0324	231.0	231	209	218	220	209.0
SV0325	231.0	231	265	128	124	232.0
SV0326	231.0	231	225	143	140	213.0
SV0327	231.0	231	258	120	117	230.0
SV0328	231.0	231	252	118	119	225.0
SV0329	231.0	231	255	241	244	240.0
SV0330	231.0	231	245	262	261	215.0
SV0331	231.0	231	254	227	224	236.0
SV0332	231.0	231	253	221	217	238.0
SV0333	231.0	231	232	126	126	217.0
SV0401	91.0	163	160	96	102	163.0
SV0402	183.0	190	213	65	43	165.0
SV0403	83.0	153	134	37	49	162.0
SV0404	66.0	144	148	208	209	160.0
SV0405	18.5	74	104	253	254	155.0
SV0406	18.5	90	103	199	200	157.0
SV0407	231.0	231	220	195	194	166.0
SV0408	18.5	62	81	69	47	154.0
SV0409	38.0	52	61	95	103	153.0
SV0410	51.0	65	87	64	44	156.0
SV0411	81.0	156	143	36	50	161.0
SV0412	104.0	169	204	209	208	158.0
SV0413	50.0	127	137	254	253	159.0
SV0414	118.0	182	184	200	199	164.0
SV0415	54.0	81	9	194	195	152.0
SV0416	18.5	9	30	68	48	151.0
SV0501	185.0	188	238	46	55	42.0
SV0502	32.0	45	85	16	8	28.0
SV0503	99.0	93	150	9	10	38.5
SV0504	115.0	143	175	153	153	37.0
SV0505	3.0	5	4	176	176	21.0
SV0506	35.0	37	67	146	148	31.0
SV0507	39.0	55	101	145	144	29.5
SV0508	27.0	23	33	18	14	25.0
SV0509	97.0	116	76	44	57	34.0
SV0510	231.0	231	165	17	7	36.0
SV0511	82.0	125	140	8	11	40.0
SV0512	117.0	148	89	154	152	33.0
SV0513	116.0	145	80	177	175	35.0
SV0514	100.0	126	190	148	146	38.5
SV0515	5.0	19	74	144	145	29.5
SV0516	14.0	10	44	19	13	26.0
SV0517	52.0	50	48	45	56	32.0
SV0518	2.0	3	19	15	9	22.0
SV0519	33.0	43	45	7	12	27.0
SV0520	23.0	24	28	152	154	23.0
SV0521	6.0	14	47	175	177	24.0
SV0522	98.0	123	187	147	147	41.0
SV0601	111.0	167	199	170	168	115.0
SV0602	69.0	91	135	34	28	108.0
SV0603	11.0	6	31	51	70	99.0
SV0604	11.0	16	39	31	18	100.0
SV0605	86.0	100	141	12	20	112.0
SV0606	46.0	75	142	183	185	102.0
SV0607	11.0	1	17	234	235	94.0
SV0608	11.0	13	56	173	174	97.0
SV0609	11.0	7	42	168	170	98.0
SV0610	34.0	30	60	33	29	101.0
SV0611	93.0	142	152	52	69	114.0
SV0612	31.0	32	59	30	19	96.0
SV0613	15.0	21	66	11	21	103.5
SV0614	92.0	110	95	184	184	110.0
SV0615	21.0	58	121	235	234	107.0
SV0616	47.0	41	99	174	173	109.0
SV0617	28.0	49	107	169	169	103.5
SV0618	103.0	140	208	35	27	113.0
SV0619	30.0	54	123	50	71	111.0
SV0620	70.0	68	54	32	17	105.0
SV0621	16.0	20	58	10	22	95.0
SV0622	148.0	166	36	185	183	106.0
SV0701	170.0	133	203	258	256	192.0
SV0702	170.0	106	192	204	205	181.0
SV0703	170.0	70	173	201	202	180.0
SV0704	177.5	179	197	106	98	191.0
SV0705	163.0	162	201	123	129	187.0
SV0706	179.0	99	177	99	93	183.0
SV0707	170.0	160	224	89	97	194.0
SV0708	170.0	170	227	213	212	195.0
SV0709	177.5	77	178	257	257	185.0
SV0710	194.0	194	259	205	204	197.0
SV0711	170.0	122	185	202	201	193.0
SV0712	170.0	141	181	105	99	184.0
SV0713	170.0	115	172	124	128	186.0
SV0715	170.0	161	221	100	92	189.0
SV0716	170.0	175	229	90	96	196.0
SV0717	170.0	134	215	212	213	190.0
SV0718	170.0	109	195	256	258	182.0
SV0801	231.0	231	120	239	238	251.0
SV0802	231.0	231	82	231	232	254.0
SV0803	231.0	231	112	92	67	257.0
SV0804	231.0	231	147	102	108	262.0
SV0805	231.0	231	119	86	63	252.0
SV0806	231.0	231	146	49	58	263.0
SV0807	231.0	231	96	248	250	245.0
SV0808	231.0	231	131	266	264	248.0
SV0809	231.0	231	94	238	239	247.0
SV0810	231.0	231	118	230	233	250.0
SV0811	231.0	231	114	91	68	243.0
SV0812	231.0	231	153	103	107	265.0
SV0813	231.0	231	138	87	62	249.0
SV0814	231.0	231	113	48	59	253.0
SV0815	231.0	231	193	250	248	258.0
SV0816	231.0	231	78	265	265	241.0
SV0817	231.0	231	133	240	237	259.0
SV0818	231.0	231	100	233	231	255.0
SV0819	231.0	231	161	93	66	266.0
SV0820	231.0	231	169	104	106	264.0
SV0821	231.0	231	139	88	61	261.0
SV0822	231.0	231	124	47	60	246.0
SV0823	231.0	231	116	249	249	260.0
SV0824	231.0	231	75	264	266	242.0
SV0825	231.0	231	64	237	240	244.0
SV0826	231.0	231	186	232	230	256.0
SV0901	119.5	114	200	80	87	84.0
SV0902	126.0	67	170	111	114	79.0
SV0903	126.0	94	180	74	72	85.0
SV0904	126.0	83	176	61	84	86.0
SV0905	132.0	137	154	182	181	82.5
SV0906	126.0	87	188	229	228	82.5
SV0907	157.0	172	226	171	172	88.0
SV0908	126.0	82	179	165	164	81.0
SV0909	126.0	120	207	81	86	89.0
SV0910	231.0	231	262	113	112	93.0
SV0911	126.0	35	145	72	74	76.0
SV0912	182.0	187	248	62	83	92.0
SV0913	126.0	64	157	181	182	77.0
SV0914	126.0	33	126	228	229	75.0
SV0915	161.0	185	235	172	171	90.0
SV0916	126.0	85	167	164	165	80.0
SV0917	231.0	231	168	79	88	87.0
SV0918	187.0	193	257	112	113	91.0
SV0919	126.0	42	98	73	73	74.0
SV0920	119.5	104	194	60	85	78.0
SV1001	73.0	129	88	139	137	6.0
SV1002	162.0	183	151	151	149	15.0
SV1003	231.0	231	250	136	134	20.0
SV1004	231.0	231	242	133	131	19.0
SV1019	112.0	154	182	6	5	13.5
SV1005	7.0	38	8	28	42	2.0
SV1006	29.0	71	35	3	2	4.0
SV1007	101.0	132	164	1	4	13.5
SV1099	1.0	8	14	137	139	5.0
SV1008	184.0	191	202	150	150	18.0
SV1009	71.0	97	52	134	136	7.0
SV1010	77.0	111	4	131	133	1.0
SV1011	36.0	107	34	5	6	3.0
SV1012	231.0	231	233	29	41	12.0
SV1013	113.0	135	97	4	1	10.0
SV1014	160.0	178	155	2	3	17.0
SV1015	26.0	76	73	138	138	9.0
SV1016	49.0	88	84	149	151	11.0
SV1017	114.0	118	129	135	135	16.0
SV1018	88.0	102	46	132	132	8.0
SV1101	72.0	61	50	71	51	168.0
SV1102	107.0	139	163	97	105	173.0
SV1103	109.0	176	206	67	45	177.0
SV1104	60.5	34	32	38	54	169.0
SV1105	60.5	26	21	211	210	171.0
SV1106	60.5	112	130	255	255	176.0
SV1107	96.0	117	109	203	203	174.0
SV1108	105.0	136	53	198	198	172.0
SV1109	55.0	18	11	70	52	167.0
SV1110	108.0	165	171	98	104	179.0
SV1111	64.5	27	51	66	46	170.0
SV1112	94.0	151	136	39	53	178.0
SV1113	56.5	96	122	210	211	175.0
SV1201	41.0	12	77	206	207	50.0
SV1202	150.0	168	92	158	157	56.0
SV1203	68.0	56	93	155	156	51.0
SV1204	74.0	63	106	24	24	47.0
SV1205	80.0	66	144	53	76	55.0
SV1299	41.0	48	91	22	16	52.0
SV1298	43.5	2	26	13	26	44.0
SV1206	41.0	11	37	159	159	43.0
SV1207	155.0	177	13	207	206	45.0
SV1208	43.5	15	69	157	158	46.0
SV1209	106.0	146	174	156	155	57.0
SV1210	186.0	192	115	25	23	54.0
SV1211	45.0	29	90	54	75	49.0
SV1212	53.0	46	108	23	15	53.0
SV1213	89.0	92	43	14	25	48.0
SV1301	76.0	78	63	166	167	68.0
SV1302	48.0	36	55	214	215	69.0
SV1303	22.0	25	18	162	163	58.0
SV1304	4.0	4	4	160	161	59.0
SV1305	90.0	105	40	43	32	66.0
SV1306	25.0	31	41	77	90	63.0
SV1307	78.0	84	79	41	30	70.0
SV1308	85.0	101	27	20	35	60.0
SV1309	181.0	189	216	167	166	73.0
SV1310	75.0	98	125	215	214	71.0
SV1311	79.0	86	12	163	162	61.0
SV1312	24.0	44	72	161	160	64.0
SV1313	37.0	39	22	42	33	62.0
SV1314	8.0	51	65	78	89	67.0
SV1315	95.0	103	4	40	31	65.0
SV1316	102.0	155	49	21	34	72.0
SV1401	151.0	164	83	192	190	129.0
SV1402	138.0	57	4	245	247	120.0
SV1403	138.0	53	20	186	188	116.0
SV1405	149.0	149	70	179	179	126.0
SV1406	159.0	184	159	84	80	137.0
SV1407	138.0	131	86	110	109	132.0
SV1408	145.0	130	23	76	64	121.0
SV1409	146.0	128	71	57	79	125.0
SV1410	144.0	59	4	190	192	117.0
SV1411	138.0	138	57	246	246	130.0
SV1412	138.0	108	25	187	187	128.0
SV1413	153.0	173	111	180	178	135.0
SV1414	156.0	181	132	83	81	136.0
SV1415	138.0	72	4	108	111	118.0
SV1416	138.0	95	29	75	65	122.5
SV1417	158.0	180	127	59	77	134.0
SV1418	138.0	73	16	191	191	122.5
SV1419	154.0	171	68	247	245	133.0
SV1420	231.0	231	128	188	186	138.0
SV1421	138.0	119	38	178	180	127.0
SV1422	138.0	69	15	82	82	119.0
SV1423	152.0	174	62	109	110	131.0
SV0714	170.0	152	24	101	91	188.0
SV1404	138.0	124	10	58	78	124.0

Let’s write this back to the excel doc

We can now build a visualization for the comparison between different valid methods.

Index sensitivity to method

As we can see, the final ranking for each location is very sensitive to the methods.

An approach to select the method can be to identify, average ranks per method in order to identify the method that is getting closer this average ranks.

we can first visualise those average ranks.

Next is to compute standard deviation for each method.

Severity Index on a map

We can now visualize the thematic indicator on a map.

An initial visualization will be to present both the severity and the population size affected by this severity.

Annex

Leveraging Facebook data for Good dataset

People who use Facebook on a mobile device have the option of providing their precise location in order to enable products like Nearby Friends and Find Wi-Fi and to get local content and ads. Different type of products are produced by the Facebok Data for Good Team by aggregating and de-identifying this data. Only people who opt in to Location History and background location collection are included. People with very few location pings in a day are not informative for these trends, and, therefore, we include only those people whose location is observed for a meaningful period of the day.

Central America indeed has an important [number of facebook users]and Facebook data for good has released a few dataset

How to re-use that script

Get the correct project name in HDX